Corollary discharge enables proprioception from lateral line sensory feedback

Animals modulate sensory processing in concert with motor actions. Parallel copies of motor signals, called corollary discharge (CD), prepare the nervous system to process the mixture of externally and self-generated (reafferent) feedback that arises during locomotion. Commonly, CD in the peripheral nervous system cancels reafference to protect sensors and the central nervous system from being fatigued and overwhelmed by self-generated feedback. However, cancellation also limits the feedback that contributes to an animal’s awareness of its body position and motion within the environment, the sense of proprioception. We propose that, rather than cancellation, CD to the fish lateral line organ restructures reafference to maximize proprioceptive information content. Fishes’ undulatory body motions induce reafferent feedback that can encode the body’s instantaneous configuration with respect to fluid flows. We combined experimental and computational analyses of swimming biomechanics and hair cell physiology to develop a neuromechanical model of how fish can track peak body curvature, a key signature of axial undulatory locomotion. Without CD, this computation would be challenged by sensory adaptation, typified by decaying sensitivity and phase distortions with respect to an input stimulus. We find that CD interacts synergistically with sensor polarization to sharpen sensitivity along sensors’ preferred axes. The sharpening of sensitivity regulates spiking to a narrow interval coinciding with peak reafferent stimulation, which prevents adaptation and homogenizes the otherwise variable sensor output. Our integrative model reveals a vital role of CD for ensuring precise proprioceptive feedback during undulatory locomotion, which we term external proprioception.

Ephaptic Coupling in Hybrid Neuronal Model

In recent decades, there has been growing interest in the impact of electric fields generated in the brain. Transmembrane ionic currents originate electric fields in the extracellular space and are capable of affecting nearby neurons, a phenomenon called ephaptic neuronal communication. In the present work, the Quadratic Integrate-and-Trigger model (QIF-E) underwent an adjustment/improvement to include the ephaptic coupling behavior between neurons and their results are compared to the empirical results. In this way, the analysis tools are employed according to the neuronal activity regime: (i) for the subthreshold regime, the circular statistic is used to describe the phase differences between the input stimulus signal and the modeled membrane response; (ii) in the suprathreshold regime, the Population Vector and the Spike Field Coherence are employed to estimate phase preferences and the coupling intensity between the input stimulus and the Action Potentials. The results observed are i) in the subthreshold regime the values of the phase differences change with distinct frequencies of an input stimulus; ii) in the supra-threshold regime the preferential phase of Action Potentials changes for different frequencies. In addition, we explore other parameters of the model, such as noise and membrane characteristic-time, in order to understand different types of neurons and extracellular environment related to ephaptic communication. Such results are consistent with results observed in empirical experiments based on ephaptic coupling behavior. In addition, the QIF-E model allows further studies on the physiological importance of ephaptic coupling in the brain, and its simplicity can open a door to simulating ephaptic coupling in neuron networks and evaluating the impact of ephaptic communication in such scenarios.

The influence of stimulus and behavioral histories on predictive control of smooth pursuit eye movements

The pursuit system has the ability to perform predictive control of eye movements. Even when the target motion is unpredictable due to velocity or direction changes, preceding changes in eye velocity are generated based on weighted averaging of past stimulus timing. However, it is still uncertain whether behavioral history influences the control of predictive pursuit. Thus, we attempted to clarify the influences of stimulus and behavioral histories on predictive pursuit to randomized target velocity. We used alternating-ramp stimuli, where the rightward velocity was fixed while the leftward velocity was either fixed (predictable) or randomized (unpredictable). Predictive eye deceleration was observed regardless of whether the target velocity was predictable or not. In particular, the predictable condition showed that the predictive pursuit responses corresponded to future target velocity. The linear mixed-effects model showed that both stimulus and behavioral histories of the previous two or three trials had influences on the predictive pursuit responses to the unpredictable target velocity. Our results suggest that the predictive pursuit system allows to track randomized target motion using the information from previous several trials, and the information of sensory input (stimulus) and motor output (behavior) in the past time sequences have partially different influences on predictive pursuit.

A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition

Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. However, most models paid no attention to the processing time, and the ones which do, are not biologically plausible. By modifying a hierarchical spiking neural network (spiking HMAX), the input stimulus is represented temporally within the spike trains. Then, by coupling the modified spiking HMAX model, with an accumulation-to-bound decision-making model, the generated spikes are accumulated over time. The input category is determined as soon as the firing rates of accumulators reaches a threshold (decision bound). The proposed object recognition model accounts for both recognition time and accuracy. Results show that not only does the model follow human accuracy in a psychophysical task better than the well-known non-temporal models, but also it predicts human response time in each choice. Results provide enough evidence that the temporal representation of features is informative, since it can improve the accuracy of a biologically plausible decision maker over time. In addition, the decision bound is able to adjust the speed-accuracy trade-off in different object recognition tasks.

Corollary discharge prevents signal distortion and enhances sensing during locomotion

Sensory feedback during movement entails sensing a mix of externally- and self-generated stimuli (respectively, exafference and reafference). In many peripheral sensory systems, a parallel copy of the motor command, a corollary discharge, is thought to eliminate sensory feedback during behaviors. However, reafference has important roles in motor control, because it provides real-time feedback on the animal’s motions through the environment. In this case, the corollary discharge must be calibrated to enable feedback while avoiding negative consequences like sensor fatigue. The undulatory motions of fishes’ bodies generate induced flows that are sensed by the lateral line sensory organ, and prior work has shown these reafferent signals contribute to the regulation of swimming kinematics. Corollary discharge to the lateral line reduces the gain for reafference, but cannot eliminate it altogether. We develop a data-driven model integrating swimming biomechanics, hair cell physiology, and corollary discharge to understand how sensory modulation is calibrated during locomotion in larval zebrafish. In the absence of corollary discharge, lateral line afferent units exhibit the highly heterogeneous habituation rates characteristic of hair cell systems, typified by decaying sensitivity and phase distortions with respect to an input stimulus. Activation of the corollary discharge prevents habituation, reduces response heterogeneity, and regulates response phases in a narrow interval around the time of the peak stimulus. This suggests a synergistic interaction between the corollary discharge and the polarization of lateral line sensors, which sharpens sensitivity along their preferred axes. Our integrative model reveals a vital role of corollary discharge for ensuring precise feedback, including proprioception, during undulatory locomotion.

SPIKING ACTIVITY OF LIFH NEURON MODEL WITH VARIABLE INPUT STIMULUS

Neuronal information processing occurs in term of spikes. A neuron can emits various kinds of spiking patterns based on the applied input stimulus. In this article, we study the spiking pattern of LIFH neuron model in the presence of four different kinds of applied input stimulus, namely, constant input stimulus, uniformly distributed input stimulus, Gaussian distributed input stimulus and stochastic input stimulus. Here, we notice the tonic and semi-tonic spiking pattern for Gaussian distributed input stimulus and stochastic input stimulus.

Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

A tactile sensor system with sensory neurons and a perceptual synaptic network based on semivolatile carbon nanotube transistors

The human sensory system has a fascinating stimulus-detection capability attributed to the fact that the feature (pattern) of an input stimulus can be extracted through perceptual learning. Therefore, sensory information can be organized and identified efficiently based on iterative experiences, whereby the sensing ability is improved. Specifically, the distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. Herein, we demonstrate an artificial tactile sensor system with a sensory neuron and a perceptual synaptic network composed of a single device: a semivolatile carbon nanotube transistor. The system can differentiate the temporal features of tactile patterns, and its recognition accuracy can be improved by an iterative learning process. Furthermore, the developed circuit model of the system provides quantitative analytical and product-level feasibility. This work is a step toward the design and use of a neuromorphic sensory system with a learning capability for potential applications in robotics and prosthetics.

Full Function Sampling of Uncertain Correlations

Empirically-based correlations are commonly used in modeling and simulation but rarely have rigorous uncertainty quantification that captures the nature of the underlying data. In many applications, a mathematical description for a parameter response to some input stimulus is often either unknown, unable to be measured, or both. Likewise, the data used to observe a parameter response is often noisy, and correlations are derived to approximate the bulk response. Practitioners frequently treat the chosen correlation — sometimes referred to as the “surrogate” or “reduced-order” model of the response — as a constant mathematical description of the relationship between input and output. This assumption, as with any model, is incorrect to some degree, and the uncertainty in the correlation can potentially have significant impacts on system responses. Thus, proper treatment of correlation uncertainty is necessary. In this paper, a method is proposed for high-level abstract sampling of uncertain data correlations. Whereas uncertainty characterization is often assigned to scalar values for direct sampling, functional uncertainty is not always straightforward. A systematic approach for sampling univariable uncertain correlations was developed to perform more rigorous uncertainty analyses and more reliably sample the correlation space. This procedure implements pseudo-random sampling of a correlation with a bounded input range to maintain the correlation form, to respect variable uncertainty across the range, and to ensure function continuity with respect to the input variable.

Flexible reset and entrainment of delta oscillations in primate primary auditory cortex: modeling and experiment

Salient auditory stimuli typically exhibit rhythmic temporal patterns. A growing body of evidence suggests that, in primary auditory cortex (A1), attention is associated with entrainment of delta rhythms (1 – 4 Hz) by these auditory stimuli. It is thought that this entrainment involves phase reset of ongoing spontaneous oscillations in A1 by thalamus matrix afferents, but precise mechanisms are unknown. Furthermore, naturalistic stimuli can vary widely in terms of their rhythmicity: some cycles can be longer than others and frequency can drift over time. It is not clear how the auditory system accommodates this natural variability. We show that in rhesus macaque monkey A1 in vivo, bottom-up gamma (40 Hz) click trains influence ongoing spontaneous delta rhythms by inducing an initial delta-timescale transient response, followed by entrainment to gamma and suppression of delta. We then construct a computational model to reproduce this effect, showing that transient thalamus matrix activation can reset A1 delta oscillations by directly activating deep (layer 5) IB cells, promoting bursting, and beginning a new delta cycle. In contrast, long duration gamma-rhythmic input stimuli induce a steady-state containing entrainment of superficial RS and FS cells at gamma, and suppression of delta oscillations. This suppression is achieved in the model by two complementary pathways. First, long-duration thalamus matrix input causes IB cells to switch from bursting to sparse firing, which disrupts the IB bursts associated with delta. Second, thalamus core input activates deep FS cells (by way of layer 4), which fire at gamma frequency and actively inhibit the delta oscillator. Together, these two fundamental operations of reset and suppression can respectively advance and delay the phase of the delta oscillator, allowing it to follow rhythms exhibiting the type of variability found in the natural environment. We discuss these findings in relation to functional implications for speech processing.Author summaryNeurons organize their firing into synchronous, rhythmic patterns. These neural oscillations have been shown to entrain to rhythmic stimuli in the external world, such as patterns of speech or patterns of movement. By entraining to a particular input stimulus, these oscillations are thought to help us attend to that stimulus and to exclude others. To understand how this synchronization emerges, we constructed a physiologically detailed mathematical model of the primary auditory cortex. By fitting this model to a variety of experimental data, we suggest fundamental mechanisms by which neurons of the auditory cortex can synchronize their activity to rhythmic external stimuli. This result will be useful for understanding the mechanism and limitations of oscillatory entrainment, which are thought to underlie the processing of naturalistic auditory inputs like speech or music. Furthermore, this model, though simplified, was shown to generalize and reproduce a wide range of experimental results, and can thus be used as a starting point for building more complex models of auditory cortex.